Quantitative strategies on High Frequency Data
Elgun Ismayilov
2025-01-25
Load necessary libraries
library(xts) # For handling xts time-series objects (e.g., lag.xts, diff.xts)
library(TTR) # For technical trading rules, used here for EMA calculation
library(quantmod) # For financial modeling and charting (e.g., chartSeries)
library(PerformanceAnalytics) # Used for performance metrics (e.g., Sortino ratio)
library(tseries) # Used for maxdrawdown
library(roll) # Used for rolling statistics (e.g., roll_mean)
library(chron) # Used for handling and comparing times (e.g., times function)
library(DT) # Used for create tablesource("https://raw.githubusercontent.com/ptwojcik/HFD/master/function_positionVB.R") # Load custom function for position calculationLoad datas in sample period
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2022_Q1.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2022_Q3.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2022_Q4.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2023_Q2.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2023_Q4.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2024_Q1.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2024_Q2.RData")Load datas in sample period
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2022_Q2.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2023_Q1.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2023_Q3.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2024_Q3.RData")
load("/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/data/data1_2024_Q4.RData")Data preparation
Convert ‘time’ column to POSIXlt datetime format with GMT timezone
data1_2022_Q1$time <- strptime(data1_2022_Q1$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT") data1_2022_Q2$time <- strptime(data1_2022_Q2$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT") data1_2022_Q3$time <- strptime(data1_2022_Q3$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2022_Q4$time <- strptime(data1_2022_Q3$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2023_Q1$time <- strptime(data1_2023_Q1$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2023_Q2$time <- strptime(data1_2023_Q2$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2023_Q3$time <- strptime(data1_2023_Q3$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2023_Q4$time <- strptime(data1_2023_Q4$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2024_Q1$time <- strptime(data1_2024_Q1$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2024_Q2$time <- strptime(data1_2024_Q2$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2024_Q3$time <- strptime(data1_2024_Q3$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")data1_2024_Q4$time <- strptime(data1_2024_Q4$time,
format = "%d.%m.%Y %H:%M:%OS",
tz = "GMT")Extract time (HH:MM:SS) for the entire index
times_data_2022_Q1 <- substr(index(data1_2022_Q1), 12, 19)
times_data_2022_Q2 <- substr(index(data1_2022_Q2), 12, 19)
times_data_2022_Q3 <- substr(index(data1_2022_Q3), 12, 19)
times_data_2022_Q4 <- substr(index(data1_2022_Q4), 12, 19)
times_data_2023_Q1 <- substr(index(data1_2023_Q1), 12, 19)
times_data_2023_Q2 <- substr(index(data1_2023_Q2), 12, 19)
times_data_2023_Q3 <- substr(index(data1_2023_Q3), 12, 19)
times_data_2023_Q4 <- substr(index(data1_2023_Q4), 12, 19)
times_data_2024_Q1 <- substr(index(data1_2024_Q1), 12, 19)
times_data_2024_Q2 <- substr(index(data1_2024_Q2), 12, 19)
times_data_2024_Q3 <- substr(index(data1_2024_Q3), 12, 19)
times_data_2024_Q4 <- substr(index(data1_2024_Q4), 12, 19)Plot of price series
# Plot futures contracts for each dataset
par(mfrow = c(6, 2), mar = c(4, 4, 2, 1)) # Arrange plots in a grid layout (4 rows, 2 columns)
# Generate individual plots
plot(data1_2022_Q1$NQ, type = "l", col = "blue", main = "NQ Futures - 2022 Q1", xlab = "Time", ylab = "Price")
plot(data1_2022_Q2$NQ, type = "l", col = "blue", main = "NQ Futures - 2022 Q2", xlab = "Time", ylab = "Price")
plot(data1_2022_Q3$NQ, type = "l", col = "blue", main = "NQ Futures - 2022 Q3", xlab = "Time", ylab = "Price")
plot(data1_2022_Q4$NQ, type = "l", col = "blue", main = "NQ Futures - 2022 Q4", xlab = "Time", ylab = "Price")
plot(data1_2023_Q1$NQ, type = "l", col = "blue", main = "NQ Futures - 2023 Q1", xlab = "Time", ylab = "Price")
plot(data1_2023_Q2$NQ, type = "l", col = "blue", main = "NQ Futures - 2023 Q2", xlab = "Time", ylab = "Price")
plot(data1_2023_Q3$NQ, type = "l", col = "blue", main = "NQ Futures - 2023 Q3", xlab = "Time", ylab = "Price")
plot(data1_2023_Q4$NQ, type = "l", col = "blue", main = "NQ Futures - 2023 Q4", xlab = "Time", ylab = "Price")
plot(data1_2024_Q1$NQ, type = "l", col = "blue", main = "NQ Futures - 2024 Q1", xlab = "Time", ylab = "Price")
plot(data1_2024_Q2$NQ, type = "l", col = "blue", main = "NQ Futures - 2024 Q2", xlab = "Time", ylab = "Price")
plot(data1_2024_Q3$NQ, type = "l", col = "blue", main = "NQ Futures - 2024 Q3", xlab = "Time", ylab = "Price")
plot(data1_2024_Q4$NQ, type = "l", col = "blue", main = "NQ Futures - 2024 Q4", xlab = "Time", ylab = "Price")
# Reset layout to default
par(mfrow = c(1, 1))Strategies based on 2022 Q1
data1_2022_Q1["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2022_Q1["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2022_Q1$ema_120 <- EMA(na.locf(data1_2022_Q1$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2022_Q1$ema_120[is.na(data1_2022_Q1$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2022_Q1$position_ema_120 <- ifelse(lag.xts(data1_2022_Q1$NQ) >
lag.xts(data1_2022_Q1$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2022_Q1$position_ema_120[times(times_data_2022_Q1) <= times("9:40:00") |
times(times_data_2022_Q1) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q1$position_ema_120 <- na.locf(data1_2022_Q1$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2022_Q1$gross_profit_ema_120 <- data1_2022_Q1$position_ema_120 * diff.xts(data1_2022_Q1$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2022_Q1$gross_profit_ema_120[is.na(data1_2022_Q1$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2022_Q1$ntrans_ema_120 <- abs(diff.xts(data1_2022_Q1$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q1$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q1$net_profit_ema_120 <- data1_2022_Q1$gross_profit_ema_120 - data1_2022_Q1$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2022_Q1$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q1$mean_60 <- roll_mean(na.locf(data1_2022_Q1$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q1$sd_120 <- roll_sd(na.locf(data1_2022_Q1$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2022_Q1$position_sd_ema <-
positionVB(signal = data1_2022_Q1$NQ,
upper = data1_2022_Q1$mean_60 + 1.5 * data1_2022_Q1$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2022_Q1$mean_60 - 1.5 * data1_2022_Q1$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2022_Q1, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q1$position_sd_ema <- na.locf(data1_2022_Q1$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2022_Q1$gross_profit_sd_ema <- data1_2022_Q1$position_sd_ema * diff.xts(data1_2022_Q1$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2022_Q1$gross_profit_sd_ema[is.na(data1_2022_Q1$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2022_Q1$ntrans_sd_ema <- abs(diff.xts(data1_2022_Q1$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q1$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q1$net_profit_sd_ema <- data1_2022_Q1$gross_profit_sd_ema - data1_2022_Q1$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2022_Q1$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2022 Q2
data1_2022_Q2["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2022_Q2["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2022_Q2$ema_120 <- EMA(na.locf(data1_2022_Q2$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2022_Q2$ema_120[is.na(data1_2022_Q2$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2022_Q2$position_ema_120 <- ifelse(lag.xts(data1_2022_Q2$NQ) >
lag.xts(data1_2022_Q2$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2022_Q2$position_ema_120[times(times_data_2022_Q2) <= times("9:40:00") |
times(times_data_2022_Q2) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q2$position_ema_120 <- na.locf(data1_2022_Q2$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2022_Q2$gross_profit_ema_120 <- data1_2022_Q2$position_ema_120 * diff.xts(data1_2022_Q2$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2022_Q2$gross_profit_ema_120[is.na(data1_2022_Q2$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2022_Q2$ntrans_ema_120 <- abs(diff.xts(data1_2022_Q2$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q2$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q2$net_profit_ema_120 <- data1_2022_Q2$gross_profit_ema_120 - data1_2022_Q2$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2022_Q2$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q2$mean_60 <- roll_mean(na.locf(data1_2022_Q2$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q2$sd_120 <- roll_sd(na.locf(data1_2022_Q2$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2022_Q2$position_sd_ema <-
positionVB(signal = data1_2022_Q2$NQ,
upper = data1_2022_Q2$mean_60 + 1.5 * data1_2022_Q2$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2022_Q2$mean_60 - 1.5 * data1_2022_Q2$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2022_Q2, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q2$position_sd_ema <- na.locf(data1_2022_Q2$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2022_Q2$gross_profit_sd_ema <- data1_2022_Q2$position_sd_ema * diff.xts(data1_2022_Q2$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2022_Q2$gross_profit_sd_ema[is.na(data1_2022_Q2$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2022_Q2$ntrans_sd_ema <- abs(diff.xts(data1_2022_Q2$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q2$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q2$net_profit_sd_ema <- data1_2022_Q2$gross_profit_sd_ema - data1_2022_Q2$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2022_Q2$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2022 Q3
data1_2022_Q3["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2022_Q3["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2022_Q3$ema_120 <- EMA(na.locf(data1_2022_Q3$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2022_Q3$ema_120[is.na(data1_2022_Q3$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2022_Q3$position_ema_120 <- ifelse(lag.xts(data1_2022_Q3$NQ) >
lag.xts(data1_2022_Q3$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2022_Q3$position_ema_120[times(times_data_2022_Q3) <= times("9:40:00") |
times(times_data_2022_Q3) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q3$position_ema_120 <- na.locf(data1_2022_Q3$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2022_Q3$gross_profit_ema_120 <- data1_2022_Q3$position_ema_120 * diff.xts(data1_2022_Q3$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2022_Q3$gross_profit_ema_120[is.na(data1_2022_Q3$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2022_Q3$ntrans_ema_120 <- abs(diff.xts(data1_2022_Q3$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q3$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q3$net_profit_ema_120 <- data1_2022_Q3$gross_profit_ema_120 - data1_2022_Q3$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2022_Q3$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q3$mean_60 <- roll_mean(na.locf(data1_2022_Q3$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q3$sd_120 <- roll_sd(na.locf(data1_2022_Q3$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2022_Q3$position_sd_ema <-
positionVB(signal = data1_2022_Q3$NQ,
upper = data1_2022_Q3$mean_60 + 1.5 * data1_2022_Q3$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2022_Q3$mean_60 - 1.5 * data1_2022_Q3$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2022_Q3, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q3$position_sd_ema <- na.locf(data1_2022_Q3$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2022_Q3$gross_profit_sd_ema <- data1_2022_Q3$position_sd_ema * diff.xts(data1_2022_Q3$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2022_Q3$gross_profit_sd_ema[is.na(data1_2022_Q3$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2022_Q3$ntrans_sd_ema <- abs(diff.xts(data1_2022_Q3$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q3$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q3$net_profit_sd_ema <- data1_2022_Q3$gross_profit_sd_ema - data1_2022_Q3$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2022_Q3$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2022 Q4
data1_2022_Q4["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2022_Q4["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2022_Q4$ema_120 <- EMA(na.locf(data1_2022_Q4$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2022_Q4$ema_120[is.na(data1_2022_Q4$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2022_Q4$position_ema_120 <- ifelse(lag.xts(data1_2022_Q4$NQ) >
lag.xts(data1_2022_Q4$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2022_Q4$position_ema_120[times(times_data_2022_Q4) <= times("9:40:00") |
times(times_data_2022_Q4) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q4$position_ema_120 <- na.locf(data1_2022_Q4$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2022_Q4$gross_profit_ema_120 <- data1_2022_Q4$position_ema_120 * diff.xts(data1_2022_Q4$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2022_Q4$gross_profit_ema_120[is.na(data1_2022_Q4$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2022_Q4$ntrans_ema_120 <- abs(diff.xts(data1_2022_Q4$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q4$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q4$net_profit_ema_120 <- data1_2022_Q4$gross_profit_ema_120 - data1_2022_Q4$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2022_Q4$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q4$mean_60 <- roll_mean(na.locf(data1_2022_Q4$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2022_Q4$sd_120 <- roll_sd(na.locf(data1_2022_Q4$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2022_Q4$position_sd_ema <-
positionVB(signal = data1_2022_Q4$NQ,
upper = data1_2022_Q4$mean_60 + 1.5 * data1_2022_Q4$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2022_Q4$mean_60 - 1.5 * data1_2022_Q4$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2022_Q4, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2022_Q4$position_sd_ema <- na.locf(data1_2022_Q4$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2022_Q4$gross_profit_sd_ema <- data1_2022_Q4$position_sd_ema * diff.xts(data1_2022_Q4$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2022_Q4$gross_profit_sd_ema[is.na(data1_2022_Q4$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2022_Q4$ntrans_sd_ema <- abs(diff.xts(data1_2022_Q4$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2022_Q4$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2022_Q4$net_profit_sd_ema <- data1_2022_Q4$gross_profit_sd_ema - data1_2022_Q4$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2022_Q4$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2023 Q1
data1_2023_Q1["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2023_Q1["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2023_Q1$ema_120 <- EMA(na.locf(data1_2023_Q1$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2023_Q1$ema_120[is.na(data1_2023_Q1$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2023_Q1$position_ema_120 <- ifelse(lag.xts(data1_2023_Q1$NQ) >
lag.xts(data1_2023_Q1$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2023_Q1$position_ema_120[times(times_data_2023_Q1) <= times("9:40:00") |
times(times_data_2023_Q1) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q1$position_ema_120 <- na.locf(data1_2023_Q1$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2023_Q1$gross_profit_ema_120 <- data1_2023_Q1$position_ema_120 * diff.xts(data1_2023_Q1$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2023_Q1$gross_profit_ema_120[is.na(data1_2023_Q1$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2023_Q1$ntrans_ema_120 <- abs(diff.xts(data1_2023_Q1$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q1$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q1$net_profit_ema_120 <- data1_2023_Q1$gross_profit_ema_120 - data1_2023_Q1$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2023_Q1$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q1$mean_60 <- roll_mean(na.locf(data1_2023_Q1$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q1$sd_120 <- roll_sd(na.locf(data1_2023_Q1$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2023_Q1$position_sd_ema <-
positionVB(signal = data1_2023_Q1$NQ,
upper = data1_2023_Q1$mean_60 + 1.5 * data1_2023_Q1$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2023_Q1$mean_60 - 1.5 * data1_2023_Q1$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2023_Q1, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q1$position_sd_ema <- na.locf(data1_2023_Q1$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2023_Q1$gross_profit_sd_ema <- data1_2023_Q1$position_sd_ema * diff.xts(data1_2023_Q1$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2023_Q1$gross_profit_sd_ema[is.na(data1_2023_Q1$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2023_Q1$ntrans_sd_ema <- abs(diff.xts(data1_2023_Q1$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q1$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q1$net_profit_sd_ema <- data1_2023_Q1$gross_profit_sd_ema - data1_2023_Q1$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2023_Q1$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2023 Q2
data1_2023_Q2["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2023_Q2["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2023_Q2$ema_120 <- EMA(na.locf(data1_2023_Q2$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2023_Q2$ema_120[is.na(data1_2023_Q2$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2023_Q2$position_ema_120 <- ifelse(lag.xts(data1_2023_Q2$NQ) >
lag.xts(data1_2023_Q2$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2023_Q2$position_ema_120[times(times_data_2023_Q2) <= times("9:40:00") |
times(times_data_2023_Q2) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q2$position_ema_120 <- na.locf(data1_2023_Q2$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2023_Q2$gross_profit_ema_120 <- data1_2023_Q2$position_ema_120 * diff.xts(data1_2023_Q2$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2023_Q2$gross_profit_ema_120[is.na(data1_2023_Q2$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2023_Q2$ntrans_ema_120 <- abs(diff.xts(data1_2023_Q2$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q2$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q2$net_profit_ema_120 <- data1_2023_Q2$gross_profit_ema_120 - data1_2023_Q2$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2023_Q2$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q2$mean_60 <- roll_mean(na.locf(data1_2023_Q2$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q2$sd_120 <- roll_sd(na.locf(data1_2023_Q2$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2023_Q2$position_sd_ema <-
positionVB(signal = data1_2023_Q2$NQ,
upper = data1_2023_Q2$mean_60 + 1.5 * data1_2023_Q2$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2023_Q2$mean_60 - 1.5 * data1_2023_Q2$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2023_Q2, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q2$position_sd_ema <- na.locf(data1_2023_Q2$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2023_Q2$gross_profit_sd_ema <- data1_2023_Q2$position_sd_ema * diff.xts(data1_2023_Q2$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2023_Q2$gross_profit_sd_ema[is.na(data1_2023_Q2$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2023_Q2$ntrans_sd_ema <- abs(diff.xts(data1_2023_Q2$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q2$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q2$net_profit_sd_ema <- data1_2023_Q2$gross_profit_sd_ema - data1_2023_Q2$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2023_Q2$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2023 Q3
data1_2023_Q3["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2023_Q3["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2023_Q3$ema_120 <- EMA(na.locf(data1_2023_Q3$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2023_Q3$ema_120[is.na(data1_2023_Q3$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2023_Q3$position_ema_120 <- ifelse(lag.xts(data1_2023_Q3$NQ) >
lag.xts(data1_2023_Q3$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2023_Q3$position_ema_120[times(times_data_2023_Q3) <= times("9:40:00") |
times(times_data_2023_Q3) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q3$position_ema_120 <- na.locf(data1_2023_Q3$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2023_Q3$gross_profit_ema_120 <- data1_2023_Q3$position_ema_120 * diff.xts(data1_2023_Q3$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2023_Q3$gross_profit_ema_120[is.na(data1_2023_Q3$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2023_Q3$ntrans_ema_120 <- abs(diff.xts(data1_2023_Q3$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q3$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q3$net_profit_ema_120 <- data1_2023_Q3$gross_profit_ema_120 - data1_2023_Q3$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2023_Q3$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q3$mean_60 <- roll_mean(na.locf(data1_2023_Q3$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q3$sd_120 <- roll_sd(na.locf(data1_2023_Q3$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2023_Q3$position_sd_ema <-
positionVB(signal = data1_2023_Q3$NQ,
upper = data1_2023_Q3$mean_60 + 1.5 * data1_2023_Q3$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2023_Q3$mean_60 - 1.5 * data1_2023_Q3$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2023_Q3, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q3$position_sd_ema <- na.locf(data1_2023_Q3$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2023_Q3$gross_profit_sd_ema <- data1_2023_Q3$position_sd_ema * diff.xts(data1_2023_Q3$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2023_Q3$gross_profit_sd_ema[is.na(data1_2023_Q3$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2023_Q3$ntrans_sd_ema <- abs(diff.xts(data1_2023_Q3$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q3$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q3$net_profit_sd_ema <- data1_2023_Q3$gross_profit_sd_ema - data1_2023_Q3$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2023_Q3$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2023 Q4
data1_2023_Q4["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2023_Q4["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2023_Q4$ema_120 <- EMA(na.locf(data1_2023_Q4$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2023_Q4$ema_120[is.na(data1_2023_Q4$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2023_Q4$position_ema_120 <- ifelse(lag.xts(data1_2023_Q4$NQ) >
lag.xts(data1_2023_Q4$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2023_Q4$position_ema_120[times(times_data_2023_Q2) <= times("9:40:00") |
times(times_data_2023_Q2) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q4$position_ema_120 <- na.locf(data1_2023_Q4$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2023_Q4$gross_profit_ema_120 <- data1_2023_Q4$position_ema_120 * diff.xts(data1_2023_Q4$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2023_Q4$gross_profit_ema_120[is.na(data1_2023_Q4$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2023_Q4$ntrans_ema_120 <- abs(diff.xts(data1_2023_Q4$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q4$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q4$net_profit_ema_120 <- data1_2023_Q4$gross_profit_ema_120 - data1_2023_Q4$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2023_Q4$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q4$mean_60 <- roll_mean(na.locf(data1_2023_Q4$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2023_Q4$sd_120 <- roll_sd(na.locf(data1_2023_Q4$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2023_Q4$position_sd_ema <-
positionVB(signal = data1_2023_Q4$NQ,
upper = data1_2023_Q4$mean_60 + 1.5 * data1_2023_Q4$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2023_Q4$mean_60 - 1.5 * data1_2023_Q4$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2023_Q4, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2023_Q4$position_sd_ema <- na.locf(data1_2023_Q4$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2023_Q4$gross_profit_sd_ema <- data1_2023_Q4$position_sd_ema * diff.xts(data1_2023_Q4$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2023_Q4$gross_profit_sd_ema[is.na(data1_2023_Q4$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2023_Q4$ntrans_sd_ema <- abs(diff.xts(data1_2023_Q4$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2023_Q4$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2023_Q4$net_profit_sd_ema <- data1_2023_Q4$gross_profit_sd_ema - data1_2023_Q4$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2023_Q4$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2024 Q1
data1_2024_Q1["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2024_Q1["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2024_Q1$ema_120 <- EMA(na.locf(data1_2024_Q1$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2024_Q1$ema_120[is.na(data1_2024_Q1$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2024_Q1$position_ema_120 <- ifelse(lag.xts(data1_2024_Q1$NQ) >
lag.xts(data1_2024_Q1$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2024_Q1$position_ema_120[times(times_data_2024_Q1) <= times("9:40:00") |
times(times_data_2024_Q1) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q1$position_ema_120 <- na.locf(data1_2024_Q1$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2024_Q1$gross_profit_ema_120 <- data1_2024_Q1$position_ema_120 * diff.xts(data1_2024_Q1$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2024_Q1$gross_profit_ema_120[is.na(data1_2024_Q1$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2024_Q1$ntrans_ema_120 <- abs(diff.xts(data1_2024_Q1$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q1$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q1$net_profit_ema_120 <- data1_2024_Q1$gross_profit_ema_120 - data1_2024_Q1$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2024_Q1$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q1$mean_60 <- roll_mean(na.locf(data1_2024_Q1$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q1$sd_120 <- roll_sd(na.locf(data1_2024_Q1$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2024_Q1$position_sd_ema <-
positionVB(signal = data1_2024_Q1$NQ,
upper = data1_2024_Q1$mean_60 + 1.5 * data1_2024_Q1$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2024_Q1$mean_60 - 1.5 * data1_2024_Q1$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2024_Q1, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q1$position_sd_ema <- na.locf(data1_2024_Q1$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2024_Q1$gross_profit_sd_ema <- data1_2024_Q1$position_sd_ema * diff.xts(data1_2024_Q1$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2024_Q1$gross_profit_sd_ema[is.na(data1_2024_Q1$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2024_Q1$ntrans_sd_ema <- abs(diff.xts(data1_2024_Q1$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q1$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q1$net_profit_sd_ema <- data1_2024_Q1$gross_profit_sd_ema - data1_2024_Q1$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2024_Q1$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2024 Q2
data1_2024_Q2["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2024_Q2["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2024_Q2$ema_120 <- EMA(na.locf(data1_2024_Q2$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2024_Q2$ema_120[is.na(data1_2024_Q2$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2024_Q2$position_ema_120 <- ifelse(lag.xts(data1_2024_Q2$NQ) >
lag.xts(data1_2024_Q2$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2024_Q2$position_ema_120[times(times_data_2024_Q2) <= times("9:40:00") |
times(times_data_2024_Q2) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q2$position_ema_120 <- na.locf(data1_2024_Q2$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2024_Q2$gross_profit_ema_120 <- data1_2024_Q2$position_ema_120 * diff.xts(data1_2024_Q2$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2024_Q2$gross_profit_ema_120[is.na(data1_2024_Q2$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2024_Q2$ntrans_ema_120 <- abs(diff.xts(data1_2024_Q2$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q2$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q2$net_profit_ema_120 <- data1_2024_Q2$gross_profit_ema_120 - data1_2024_Q2$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2024_Q2$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q2$mean_60 <- roll_mean(na.locf(data1_2024_Q2$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q2$sd_120 <- roll_sd(na.locf(data1_2024_Q2$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2024_Q2$position_sd_ema <-
positionVB(signal = data1_2024_Q2$NQ,
upper = data1_2024_Q2$mean_60 + 1.5 * data1_2024_Q2$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2024_Q2$mean_60 - 1.5 * data1_2024_Q2$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2024_Q2, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q2$position_sd_ema <- na.locf(data1_2024_Q2$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2024_Q2$gross_profit_sd_ema <- data1_2024_Q2$position_sd_ema * diff.xts(data1_2024_Q2$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2024_Q2$gross_profit_sd_ema[is.na(data1_2024_Q2$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2024_Q2$ntrans_sd_ema <- abs(diff.xts(data1_2024_Q2$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q2$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q2$net_profit_sd_ema <- data1_2024_Q2$gross_profit_sd_ema - data1_2024_Q2$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2024_Q2$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2024 Q3
data1_2024_Q3["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2024_Q3["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2024_Q3$ema_120 <- EMA(na.locf(data1_2024_Q3$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2024_Q3$ema_120[is.na(data1_2024_Q3$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2024_Q3$position_ema_120 <- ifelse(lag.xts(data1_2024_Q3$NQ) >
lag.xts(data1_2024_Q3$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2024_Q3$position_ema_120[times(times_data_2024_Q3) <= times("9:40:00") |
times(times_data_2024_Q3) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q3$position_ema_120 <- na.locf(data1_2024_Q3$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2024_Q3$gross_profit_ema_120 <- data1_2024_Q3$position_ema_120 * diff.xts(data1_2024_Q3$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2024_Q3$gross_profit_ema_120[is.na(data1_2024_Q3$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2024_Q3$ntrans_ema_120 <- abs(diff.xts(data1_2024_Q3$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q3$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q3$net_profit_ema_120 <- data1_2024_Q3$gross_profit_ema_120 - data1_2024_Q3$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2024_Q3$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q3$mean_60 <- roll_mean(na.locf(data1_2024_Q3$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q3$sd_120 <- roll_sd(na.locf(data1_2024_Q3$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2024_Q3$position_sd_ema <-
positionVB(signal = data1_2024_Q3$NQ,
upper = data1_2024_Q3$mean_60 + 1.5 * data1_2024_Q3$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2024_Q3$mean_60 - 1.5 * data1_2024_Q3$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2024_Q3, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q3$position_sd_ema <- na.locf(data1_2024_Q3$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2024_Q3$gross_profit_sd_ema <- data1_2024_Q3$position_sd_ema * diff.xts(data1_2024_Q3$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2024_Q3$gross_profit_sd_ema[is.na(data1_2024_Q3$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2024_Q3$ntrans_sd_ema <- abs(diff.xts(data1_2024_Q3$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q3$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q3$net_profit_sd_ema <- data1_2024_Q3$gross_profit_sd_ema - data1_2024_Q3$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2024_Q3$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
Strategies based on 2024 Q4
data1_2024_Q4["T09:31/T09:35",] <- NA # Remove data between 09:31 and 09:35 by setting it to NA
data1_2024_Q4["T15:56/T16:00",] <- NA # Remove data between 15:56 and 16:00 by setting it to NASingle EMA of ‘NQ’ with a 120-period window
data1_2024_Q4$ema_120 <- EMA(na.locf(data1_2024_Q4$NQ,
na.rm = FALSE),
120) # Calculate the 120-period EMA of 'NQ' after filling missing values using the last observation carried forwarddata1_2024_Q4$ema_120[is.na(data1_2024_Q4$NQ)] <- NA # Set EMA values to NA where the original 'NQ' values are missingdata1_2024_Q4$position_ema_120 <- ifelse(lag.xts(data1_2024_Q4$NQ) >
lag.xts(data1_2024_Q4$ema_120), 1, -1) # Generate trading positions: 1 if 'NQ' is above its lagged EMA, -1 otherwise# Set trading positions to 0 outside the active trading window (before 09:40:00 or after 15:50:00)
data1_2024_Q4$position_ema_120[times(times_data_2024_Q4) <= times("9:40:00") |
times(times_data_2024_Q4) > times("15:50:00")] <- 0# Fill missing positions with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q4$position_ema_120 <- na.locf(data1_2024_Q4$position_ema_120,
na.rm = FALSE)Gross profit for Single EMA
# Calculate gross profit based on the position (ema_120), the change in 'NQ' prices, and a factor of 20
data1_2024_Q4$gross_profit_ema_120 <- data1_2024_Q4$position_ema_120 * diff.xts(data1_2024_Q4$NQ) * 20# Set any missing values in 'gross_profit_ema_120' to 0
data1_2024_Q4$gross_profit_ema_120[is.na(data1_2024_Q4$gross_profit_ema_120)] <- 0# Identify transactions by calculating the absolute change in position (every time the position changes)
data1_2024_Q4$ntrans_ema_120 <- abs(diff.xts(data1_2024_Q4$position_ema_120))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q4$ntrans_ema_120[1] <- 0Net profit for EMA 120
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q4$net_profit_ema_120 <- data1_2024_Q4$gross_profit_ema_120 - data1_2024_Q4$ntrans_ema_120 * 12# Plot the cumulative sum of the net profit to visualize the overall performance over time
chartSeries(cumsum(data1_2024_Q4$net_profit_ema_120),
name = "Cumulative Net Profit for EMA 120 Strategy")
Volatility breakout strategy using EMA 60 and SD 120
# Calculate the 60-period rolling mean of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q4$mean_60 <- roll_mean(na.locf(data1_2024_Q4$NQ, na.rm = FALSE), 60)# Calculate the 120-period rolling standard deviation of 'NQ' after filling missing values using the last observation carried forward
data1_2024_Q4$sd_120 <- roll_sd(na.locf(data1_2024_Q4$NQ, na.rm = FALSE), 120)# Measure the execution time for calculating the position based on a strategy using the 'positionVB' function
system.time(
data1_2024_Q4$position_sd_ema <-
positionVB(signal = data1_2024_Q4$NQ,
upper = data1_2024_Q4$mean_60 + 1.5 * data1_2024_Q4$sd_120, # Upper bound based on 60-period mean and 1.5 times 120-period SD
lower = data1_2024_Q4$mean_60 - 1.5 * data1_2024_Q4$sd_120, # Lower bound based on 60-period mean and 1.5 times 120-period SD
times_data = times_data_2024_Q4, # Column with times
time_lower = "09:40:00", # Start time for trading
time_upper = "15:50:00", # Exit time for all positions
strategy = "mom" # Momentum strategy
)
)# Fill missing position values with the most recent non-missing value (Last Observation Carried Forward)
data1_2024_Q4$position_sd_ema <- na.locf(data1_2024_Q4$position_sd_ema,
na.rm = FALSE)Gross profit of volatility breakout strategy
# Calculate gross profit for the strategy by multiplying position with the change in 'NQ' prices and a factor of 20
data1_2024_Q4$gross_profit_sd_ema <- data1_2024_Q4$position_sd_ema * diff.xts(data1_2024_Q4$NQ) * 20# Replace any missing values in the gross profit column with 0
data1_2024_Q4$gross_profit_sd_ema[is.na(data1_2024_Q4$gross_profit_sd_ema)] <- 0# Identify transactions by calculating the absolute change in position ('position_sd_ema')
data1_2024_Q4$ntrans_sd_ema <- abs(diff.xts(data1_2024_Q4$position_sd_ema))# Set the first transaction value to 0, as there's no previous position to compare
data1_2024_Q4$ntrans_sd_ema[1] <- 0Net profit of volatility breakout strategy
# Calculate net profit by subtracting transaction costs (based on the number of transactions and a cost factor of 12) from the gross profit
data1_2024_Q4$net_profit_sd_ema <- data1_2024_Q4$gross_profit_sd_ema - data1_2024_Q4$ntrans_sd_ema * 12# Plot the cumulative net profit over time using a chart, based on the 'net_profit_sd_ema' column
chartSeries(cumsum(data1_2024_Q4$net_profit_sd_ema),
name = "Cumulative Net Profit for Volatility Breakout Strategy (EMA 60 + SD 120)")
User function
myCalmarRatio <-myCalmarRatio <- function(x, # x = series of returns
# scale parameter = Nt
scale) {
scale * mean(coredata(x), na.rm = TRUE) /
maxdrawdown(cumsum(x))$maxdrawdown
} Write the result to CSV
Create dataframes
result_ema_120 <- data.frame(
Period = c("2022 Q1", "2022 Q2", "2022 Q3", "2022 Q4", "2023 Q1", "2023 Q2", "2023 Q3", "2023 Q4", "2024 Q1", "2024 Q2", "2024 Q3", "2024 Q4"),
Gross_SR_EMA_120 = c(SortinoRatio(data1_2022_Q1$gross_profit_ema_120),
SortinoRatio(data1_2022_Q2$gross_profit_ema_120),
SortinoRatio(data1_2022_Q3$gross_profit_ema_120),
SortinoRatio(data1_2022_Q4$gross_profit_ema_120),
SortinoRatio(data1_2023_Q1$gross_profit_ema_120),
SortinoRatio(data1_2023_Q2$gross_profit_ema_120),
SortinoRatio(data1_2023_Q3$gross_profit_ema_120),
SortinoRatio(data1_2023_Q4$gross_profit_ema_120),
SortinoRatio(data1_2024_Q1$gross_profit_ema_120),
SortinoRatio(data1_2024_Q2$gross_profit_ema_120),
SortinoRatio(data1_2024_Q3$gross_profit_ema_120),
SortinoRatio(data1_2024_Q4$gross_profit_ema_120)),
Net_SR_EMA_120 = c(SortinoRatio(data1_2022_Q1$net_profit_ema_120),
SortinoRatio(data1_2022_Q2$net_profit_ema_120),
SortinoRatio(data1_2022_Q3$net_profit_ema_120),
SortinoRatio(data1_2022_Q4$net_profit_ema_120),
SortinoRatio(data1_2023_Q1$net_profit_ema_120),
SortinoRatio(data1_2023_Q2$net_profit_ema_120),
SortinoRatio(data1_2023_Q3$net_profit_ema_120),
SortinoRatio(data1_2023_Q4$net_profit_ema_120),
SortinoRatio(data1_2024_Q1$net_profit_ema_120),
SortinoRatio(data1_2024_Q2$net_profit_ema_120),
SortinoRatio(data1_2024_Q3$net_profit_ema_120),
SortinoRatio(data1_2024_Q4$net_profit_ema_120)),
Gross_CR_EMA_120 = c(myCalmarRatio(data1_2022_Q1$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2022_Q2$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2022_Q3$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2022_Q4$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q1$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q2$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q3$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q4$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q1$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q2$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q3$gross_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q4$gross_profit_ema_120, scale = 252)),
Net_CR_EMA_120 = c(myCalmarRatio(data1_2022_Q1$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2022_Q2$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2022_Q3$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2022_Q4$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q1$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q2$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q3$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2023_Q4$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q1$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q2$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q3$net_profit_ema_120, scale = 252),
myCalmarRatio(data1_2024_Q4$net_profit_ema_120, scale = 252)),
Gross_cumPnL_EMA_120 = c(tail(cumsum(data1_2022_Q1$gross_profit_ema_120), 1),
tail(cumsum(data1_2022_Q2$gross_profit_ema_120), 1),
tail(cumsum(data1_2022_Q3$gross_profit_ema_120), 1),
tail(cumsum(data1_2022_Q4$gross_profit_ema_120), 1),
tail(cumsum(data1_2023_Q1$gross_profit_ema_120), 1),
tail(cumsum(data1_2023_Q2$gross_profit_ema_120), 1),
tail(cumsum(data1_2023_Q3$gross_profit_ema_120), 1),
tail(cumsum(data1_2023_Q4$gross_profit_ema_120), 1),
tail(cumsum(data1_2024_Q1$gross_profit_ema_120), 1),
tail(cumsum(data1_2024_Q2$gross_profit_ema_120), 1),
tail(cumsum(data1_2024_Q3$gross_profit_ema_120), 1),
tail(cumsum(data1_2024_Q4$gross_profit_ema_120), 1)),
Net_cumPnL_EMA_120 = c(tail(cumsum(data1_2022_Q1$net_profit_ema_120), 1),
tail(cumsum(data1_2022_Q2$net_profit_ema_120), 1),
tail(cumsum(data1_2022_Q3$net_profit_ema_120), 1),
tail(cumsum(data1_2022_Q4$net_profit_ema_120), 1),
tail(cumsum(data1_2023_Q1$net_profit_ema_120), 1),
tail(cumsum(data1_2023_Q2$net_profit_ema_120), 1),
tail(cumsum(data1_2023_Q3$net_profit_ema_120), 1),
tail(cumsum(data1_2023_Q4$net_profit_ema_120), 1),
tail(cumsum(data1_2024_Q1$net_profit_ema_120), 1),
tail(cumsum(data1_2024_Q2$net_profit_ema_120), 1),
tail(cumsum(data1_2024_Q3$net_profit_ema_120), 1),
tail(cumsum(data1_2024_Q4$net_profit_ema_120), 1)),
Avg_nTrades_EMA_120 = c(mean(data1_2022_Q1$ntrans_ema_120),
mean(data1_2022_Q2$ntrans_ema_120),
mean(data1_2022_Q3$ntrans_ema_120),
mean(data1_2022_Q4$ntrans_ema_120),
mean(data1_2023_Q1$ntrans_ema_120),
mean(data1_2023_Q2$ntrans_ema_120),
mean(data1_2023_Q3$ntrans_ema_120),
mean(data1_2023_Q4$ntrans_ema_120),
mean(data1_2024_Q1$ntrans_ema_120),
mean(data1_2024_Q2$ntrans_ema_120),
mean(data1_2024_Q3$ntrans_ema_120),
mean(data1_2024_Q4$ntrans_ema_120)),
Summary_Statistic_EMA_120 = c(
myCalmarRatio(data1_2022_Q1$net_profit_ema_120, scale = 252) * max(log(abs(data1_2022_Q1$net_profit_ema_120))),
myCalmarRatio(data1_2022_Q2$net_profit_ema_120, scale = 252) * max(log(abs(data1_2022_Q2$net_profit_ema_120))),
myCalmarRatio(data1_2022_Q3$net_profit_ema_120, scale = 252) * max(log(abs(data1_2022_Q3$net_profit_ema_120))),
myCalmarRatio(data1_2022_Q4$net_profit_ema_120, scale = 252) * max(log(abs(data1_2022_Q4$net_profit_ema_120))),
myCalmarRatio(data1_2023_Q1$net_profit_ema_120, scale = 252) * max(log(abs(data1_2023_Q1$net_profit_ema_120))),
myCalmarRatio(data1_2023_Q2$net_profit_ema_120, scale = 252) * max(log(abs(data1_2023_Q2$net_profit_ema_120))),
myCalmarRatio(data1_2023_Q3$net_profit_ema_120, scale = 252) * max(log(abs(data1_2023_Q3$net_profit_ema_120))),
myCalmarRatio(data1_2023_Q4$net_profit_ema_120, scale = 252) * max(log(abs(data1_2023_Q4$net_profit_ema_120))),
myCalmarRatio(data1_2024_Q1$net_profit_ema_120, scale = 252) * max(log(abs(data1_2024_Q1$net_profit_ema_120))),
myCalmarRatio(data1_2024_Q2$net_profit_ema_120, scale = 252) * max(log(abs(data1_2024_Q2$net_profit_ema_120))),
myCalmarRatio(data1_2024_Q3$net_profit_ema_120, scale = 252) * max(log(abs(data1_2024_Q3$net_profit_ema_120))),
myCalmarRatio(data1_2024_Q4$net_profit_ema_120, scale = 252) * max(log(abs(data1_2024_Q4$net_profit_ema_120)))
)
)
# Create the "Total" row
total_row <- data.frame(
Period = "Total",
Gross_SR_EMA_120 = sum(result_ema_120$Gross_SR_EMA_120, na.rm = TRUE),
Net_SR_EMA_120 = sum(result_ema_120$Net_SR_EMA_120, na.rm = TRUE),
Gross_CR_EMA_120 = sum(result_ema_120$Gross_CR_EMA_120, na.rm = TRUE),
Net_CR_EMA_120 = sum(result_ema_120$Net_CR_EMA_120, na.rm = TRUE),
Gross_cumPnL_EMA_120 = sum(result_ema_120$Gross_cumPnL_EMA_120, na.rm = TRUE),
Net_cumPnL_EMA_120 = sum(result_ema_120$Net_cumPnL_EMA_120, na.rm = TRUE),
Avg_nTrades_EMA_120 = mean(result_ema_120$Avg_nTrades_EMA_120, na.rm = TRUE),
Summary_Statistic_EMA_120 = sum(result_ema_120$Summary_Statistic_EMA_120, na.rm = TRUE),
stringsAsFactors = FALSE
)
# Ensure column names of total_row match result
colnames(total_row) <- colnames(result_ema_120)
# Append the "Total" row to the dataframe
result <- rbind(result_ema_120, total_row)
# Reset row names to remove indexing numbers
rownames(result_ema_120) <- NULLresult_ema_sd <- data.frame(
Period = c(
"2022 Q1", "2022 Q2", "2022 Q3", "2022 Q4",
"2023 Q1", "2023 Q2", "2023 Q3", "2023 Q4",
"2024 Q1", "2024 Q2", "2024 Q3", "2024 Q4"
),
Gross_SR_SD_EMA = c(
SortinoRatio(data1_2022_Q1$gross_profit_sd_ema),
SortinoRatio(data1_2022_Q2$gross_profit_sd_ema),
SortinoRatio(data1_2022_Q3$gross_profit_sd_ema),
SortinoRatio(data1_2022_Q4$gross_profit_sd_ema),
SortinoRatio(data1_2023_Q1$gross_profit_sd_ema),
SortinoRatio(data1_2023_Q2$gross_profit_sd_ema),
SortinoRatio(data1_2023_Q3$gross_profit_sd_ema),
SortinoRatio(data1_2023_Q4$gross_profit_sd_ema),
SortinoRatio(data1_2024_Q1$gross_profit_sd_ema),
SortinoRatio(data1_2024_Q2$gross_profit_sd_ema),
SortinoRatio(data1_2024_Q3$gross_profit_sd_ema),
SortinoRatio(data1_2024_Q4$gross_profit_sd_ema)
),
Net_SR_SD_EMA = c(
SortinoRatio(data1_2022_Q1$net_profit_sd_ema),
SortinoRatio(data1_2022_Q2$net_profit_sd_ema),
SortinoRatio(data1_2022_Q3$net_profit_sd_ema),
SortinoRatio(data1_2022_Q4$net_profit_sd_ema),
SortinoRatio(data1_2023_Q1$net_profit_sd_ema),
SortinoRatio(data1_2023_Q2$net_profit_sd_ema),
SortinoRatio(data1_2023_Q3$net_profit_sd_ema),
SortinoRatio(data1_2023_Q4$net_profit_sd_ema),
SortinoRatio(data1_2024_Q1$net_profit_sd_ema),
SortinoRatio(data1_2024_Q2$net_profit_sd_ema),
SortinoRatio(data1_2024_Q3$net_profit_sd_ema),
SortinoRatio(data1_2024_Q4$net_profit_sd_ema)
),
Gross_CR_SD_EMA = c(
myCalmarRatio(data1_2022_Q1$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2022_Q2$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2022_Q3$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2022_Q4$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q1$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q2$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q3$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q4$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q1$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q2$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q3$gross_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q4$gross_profit_sd_ema, scale = 252)
),
Net_CR_SD_EMA = c(
myCalmarRatio(data1_2022_Q1$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2022_Q2$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2022_Q3$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2022_Q4$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q1$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q2$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q3$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2023_Q4$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q1$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q2$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q3$net_profit_sd_ema, scale = 252),
myCalmarRatio(data1_2024_Q4$net_profit_sd_ema, scale = 252)
),
Gross_cumPnL_SD_EMA = c(
tail(cumsum(data1_2022_Q1$gross_profit_sd_ema), 1),
tail(cumsum(data1_2022_Q2$gross_profit_sd_ema), 1),
tail(cumsum(data1_2022_Q3$gross_profit_sd_ema), 1),
tail(cumsum(data1_2022_Q4$gross_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q1$gross_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q2$gross_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q3$gross_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q4$gross_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q1$gross_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q2$gross_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q3$gross_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q4$gross_profit_sd_ema), 1)
),
Net_cumPnL_SD_EMA = c(
tail(cumsum(data1_2022_Q1$net_profit_sd_ema), 1),
tail(cumsum(data1_2022_Q2$net_profit_sd_ema), 1),
tail(cumsum(data1_2022_Q3$net_profit_sd_ema), 1),
tail(cumsum(data1_2022_Q4$net_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q1$net_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q2$net_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q3$net_profit_sd_ema), 1),
tail(cumsum(data1_2023_Q4$net_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q1$net_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q2$net_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q3$net_profit_sd_ema), 1),
tail(cumsum(data1_2024_Q4$net_profit_sd_ema), 1)
),
Avg_nTrades_SD_EMA = c(
mean(data1_2022_Q1$ntrans_ema_120),
mean(data1_2022_Q2$ntrans_ema_120),
mean(data1_2022_Q3$ntrans_ema_120),
mean(data1_2022_Q4$ntrans_ema_120),
mean(data1_2023_Q1$ntrans_ema_120),
mean(data1_2023_Q2$ntrans_ema_120),
mean(data1_2023_Q3$ntrans_ema_120),
mean(data1_2023_Q4$ntrans_ema_120),
mean(data1_2024_Q1$ntrans_ema_120),
mean(data1_2024_Q2$ntrans_ema_120),
mean(data1_2024_Q3$ntrans_ema_120),
mean(data1_2024_Q4$ntrans_ema_120)
),
Summary_Statistic_SD_EMA = c(
myCalmarRatio(data1_2022_Q1$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2022_Q1$net_profit_sd_ema))),
myCalmarRatio(data1_2022_Q2$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2022_Q2$net_profit_sd_ema))),
myCalmarRatio(data1_2022_Q3$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2022_Q3$net_profit_sd_ema))),
myCalmarRatio(data1_2022_Q4$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2022_Q4$net_profit_sd_ema))),
myCalmarRatio(data1_2023_Q1$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2023_Q1$net_profit_sd_ema))),
myCalmarRatio(data1_2023_Q2$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2023_Q2$net_profit_sd_ema))),
myCalmarRatio(data1_2023_Q3$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2023_Q3$net_profit_sd_ema))),
myCalmarRatio(data1_2023_Q4$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2023_Q4$net_profit_sd_ema))),
myCalmarRatio(data1_2024_Q1$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2024_Q1$net_profit_sd_ema))),
myCalmarRatio(data1_2024_Q2$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2024_Q2$net_profit_sd_ema))),
myCalmarRatio(data1_2024_Q3$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2024_Q3$net_profit_sd_ema))),
myCalmarRatio(data1_2024_Q4$net_profit_sd_ema, scale = 252) * max(log(abs(data1_2024_Q4$net_profit_sd_ema)))
)
)
total_row <- data.frame(
Period = "Total",
Gross_SR_SD_EMA = sum(result_ema_sd$Gross_SR_SD_EMA, na.rm = TRUE),
Net_SR_SD_EMA = sum(result_ema_sd$Net_SR_SD_EMA, na.rm = TRUE),
Gross_CR_SD_EMA = sum(result_ema_sd$Gross_CR_SD_EMA, na.rm = TRUE),
Net_CR_SD_EMA = sum(result_ema_sd$Net_CR_SD_EMA, na.rm = TRUE),
Gross_cumPnL_SD_EMA = sum(result_ema_sd$Gross_cumPnL_SD_EMA, na.rm = TRUE),
Net_cumPnL_SD_EMA = sum(result_ema_sd$Net_cumPnL_SD_EMA, na.rm = TRUE),
Avg_nTrades_SD_EMA = mean(result_ema_sd$Avg_nTrades_SD_EMA, na.rm = TRUE),
Summary_Statistic_SD_EMA = sum(result_ema_sd$Summary_Statistic_SD_EMA, na.rm = TRUE),
stringsAsFactors = FALSE
)
# Ensure column names of total_row match result
colnames(total_row) <- colnames(result_ema_sd)
# Append the total row and reset row names
result_ema_sd <- rbind(result_ema_sd, total_row)
rownames(result_ema_sd) <- NULLCreate csv file for the result
final_result <- merge(result_ema_120, result_ema_sd)
write.csv(final_result, file = "/Users/elgun/Desktop/Quantitative-strategies-on-High-Frequency-Data/result.csv", row.names = FALSE)